Collaborative location method for drone vertiport and truck parking point in urban logistics

LI Zhuo-lun; LU Jian; WANG Xue-rui; LI Shan

doi:10.19818/j.cnki.1671-1637.2026.037

Volume 26 Issue 3

Mar. 2026

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LI Zhuo-lun, LU Jian, WANG Xue-rui, LI Shan. Collaborative location method for drone vertiport and truck parking point in urban logistics[J]. Journal of Traffic and Transportation Engineering, 2026, 26(3): 89-105. doi: 10.19818/j.cnki.1671-1637.2026.037

Citation:

LI Zhuo-lun, LU Jian, WANG Xue-rui, LI Shan. Collaborative location method for drone vertiport and truck parking point in urban logistics[J]. Journal of Traffic and Transportation Engineering, 2026, 26(3): 89-105. doi: 10.19818/j.cnki.1671-1637.2026.037

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Collaborative location method for drone vertiport and truck parking point in urban logistics

doi: 10.19818/j.cnki.1671-1637.2026.037

LI Zhuo-lun^{1, 2
,},
LU Jian^{1, 2
,
,},
WANG Xue-rui^{1, 2},
LI Shan^{3, 4}

1.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China
2.
Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, Jiangsu, China
3.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China
4.
College of Design and Engineering, National University of Singapore, Singapore 119077, Singapore

Funds:

National Key R & D Program of China 2023YFC3009602

Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX24_0465

More Information

Corresponding author: LU Jian, professor, PhD, E-mail: Lujian_1972@seu.edu.cn
Received Date: 2025-05-24
Accepted Date: 2025-09-28
Rev Recd Date: 2025-07-21
Publish Date: 2026-03-28

Abstract

Abstract

To enhance the operational efficiency and service quality of the synchronized truck-drone delivery (STDD) mode and to address the facility layout problem under the STDD mode, a collaborative location method for drone vertiports and truck parking points was proposed. First, based on geographic information data, the three-dimensional urban space was discretized using the grid method. By integrating indicators such as obstacle distribution, noise impact, and pedestrian accessibility, the environmental characteristics of grid units were quantitatively analyzed. Then, by considering factors such as logistics delivery distance and user demand distribution, a multi-objective drone vertiport location model was established. Furthermore, combining conditions such as vertiport service relationships and drone performance, a multi-objective truck parking point service allocation model was constructed. Finally, the fuzzy C-means clustering algorithm was employed to spatially aggregate user demand points. By considering flight safety, noise impact, and pickup efficiency, a relatively optimal vertiport layout scheme was selected from the Pareto front. Based on the distribution data of urban public parking lots, the fuzzy C-means clustering and multi-objective multi-verse optimization algorithms were integrated to obtain the optimal service matching relationship between truck parking points and vertiports. The results show that with the increase in the number of buildings served by a single vertiport, the scale of vertiports shows a decreasing trend, while the average walking distance of residents shows an increasing trend. Notably, the clustering algorithm integrated with the location optimization strategy yields an average environmental score of 0.682 for drone vertiports, representing a 49.2% improvement compared to the clustering algorithm integrated with the proximity-based location strategy. Furthermore, the number of vertiports presents a positive correlation with the number of enabled truck parking points and a negative correlation with the nearest-neighbor parking point spacing. Compared with the traditional multi-objective multi-verse optimization algorithm, the proposed algorithm reduces the number of enabled parking points by 33%, decreases the service imbalance degree by 28.6%, and increases the mean value of nearest-neighbor spacing by 22.01%. The proposed method realizes the optimized location layout of drone vertiports and truck parking points, which can provide technical support for the construction of low-altitude logistics networks in smart cities.
- low-altitude logistics,
- UAV,
- collaborative location,
- multi-objective optimization,
- service allocation,
- two-stage algorithm

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References(32)

References

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Collaborative location method for drone vertiport and truck parking point in urban logistics

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